MODIFIED INTEGRAL PROCEDURE (MIP) AS A RELIABLE SHORT-CUT METHOD IN MECHANISTIC BASED ODE KINETIC-MODEL ESTIMATION - NONISOTHERMAL AND (SEMI-)BATCH PROCESS CASES

In both small and large-scale investigations, a reliable short-cut pro cedure to estimate the approximate parameters is very useful for the s uccessive rapid checking of different Kinetic Model (KM) structures fo r their adaptation to current process data. An improved quality of the initial parameter guess improves also the reliability and the converg ence rate for a subsequent exact Nonlinear Least Squares (NLS) regress ion technique applied for fitting the final model. The recent proposed Modified Integral transformation Procedure (MIP) short-cut method of Maria and Rippin (1994) adds supplementary elements of similarity anal ysis to the classical Integral transformation Procedure (IP). By explo iting the kinetic model structure and interactive use of prior informa tion stored in a kinetic model-data-bank, the MIP makes rapid adaptati on of a KM structure and parameters, describing an already studied pro cess, to a similar process under study. The problem decomposition and the term-by-term sensitivity and estimability analysis of the model fo r various experimental data sets increase the reliability of the MIP i n reaching the global KM parameter solution region and improve the est imate quality for isothermal data cases. These results are extrapolate d in the present work for other cases, including nonisothermal linear kinetics and/or on-line recursive kinetics estimation in (semi-)batch processes. The MIP results are compared with classical short-cut metho ds, extended Kalman Filter (EKF)-based recursive estimators of differe nt complexity and exact NLS estimators.